System and method for determining a trained neural network model for scattering correction

ABSTRACT

A method for generating a trained neural network model for scanning correction corresponding to one or more imaging parameters is provided. The trained neural network model may be trained using training data. The training data may include at least one first set of training data. The first set of training data may be generated according to a process for generating the first set of training data. The process may include obtaining a first image and a second image corresponding to the one or more imaging parameters. The second image may include less scattering noises than the first image. The process may further include determine the first set of training data based on the first image and the second image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/042,536, filed on Jul. 23, 2018, designating the United States ofAmerica, which claims priority to Chinese Patent Application No201710772800.9, filed on Aug. 31, 2017, and Chinese Patent ApplicationNo 201710775674.2, filed on Aug. 31, 2017. Each of the above-referencedapplications is expressly incorporated herein by reference in theirentireties.

TECHNICAL FIELD

The present disclosure generally relates to image processing, and morespecifically, relates to systems and methods for reducing scatteringnoises in an image based on a model.

BACKGROUND

High-energy rays (e.g., X-rays, y-rays) are widely used in medicalimaging. For example, X-rays are used in, for example, computedtomography (CT) devices or digital radiography (DR) devices to generatean image of a subject. During a scan, X-rays irradiated on the subjectmay pass through the subject and be detected by a detector. However,some X-rays may scatter when passing through the subject, which maycause scattering noises in the image generated based on the scan. Thus,it is desirable to provide mechanisms for correcting an image to reduceor eliminate scattering noises.

SUMMARY

In one aspect of the present disclosure, a system is provided. Thesystem may include a storage device, and at least one processorconfigured to communicate with the storage device. The storage devicemay store a set of instructions. When executing the set of instructions,the at least one processor may be configured to direct the system togenerate a trained neural network model for scattering correctioncorresponding to one or more imaging parameters. The trained neuralnetwork model may be trained using training data. The training data mayinclude at least one first set of training data. The first set oftraining data may be generated according to a process for generating thefirst set of training data. The process may include obtaining a firstimage corresponding to the one or more imaging parameters, and obtaininga second image. The second image may include less scattering noises thanthe first image. The process may further include determining the firstset of training data based on the first image and the second image.

In some embodiments, the second image may be generated according tosecond scan data acquired by an imaging device configured with a gridunder the one or more imaging parameters, and the first image may begenerated according to first scan data acquired by the imaging devicewithout the grid under the one or more imaging parameters.Alternatively, the first image may be generated according to the firstscan data acquired by the imaging device without the grid under the oneor more imaging parameters, and the second image may be generated basedon the first image by removing at least some of the scattering noises ofthe first image. Alternatively, the first image and the second image maybe simulated according to a Monte-Carlo technique under the one or moreimaging parameters.

In some embodiments, the determining the first set of training databased on the first image and the second image may further includeprocessing the first image and the second image, and determining thefirst set of training data based on the processed first image and theprocessed second image.

In some embodiments, the first set of training data may include inputdata and tag data. The determining the first set of training data mayinclude selecting a first sub-area from the first image, and designatingimage data related to the first sub-area as the input data. Thedetermining the first set of training data may further include selectinga second sub-area corresponding to the first sub-area from the secondimage, and designating image data related to the second sub-area as thetag data.

In some embodiments, to generate the trained neural network model forscattering correction, the at least one processor may be furtherconfigured to direct the system to acquire a preliminary neural networkmodel. The at least one processor may be further configured to directthe system to determine one or more model parameters by inputting thetraining data into the preliminary neural network model. The at leastone processor may be further configured to direct the system todetermine the trained neural network model based on the one or moremodel parameters and the preliminary neural network model.

In some embodiments, the first set of training data may include inputdata and tag data. The one or more model parameters may include arelationship between the input data and the tag data.

In some embodiments, to determine the one or more model parameters, theat least one processor may be configured to direct the system to dividethe input data into a plurality of first data blocks, and divide the tagdata into a plurality of second data blocks corresponding to theplurality of first data blocks. The at least one processor may befurther configured to direct the system to determine first gradientinformation of the first data blocks, and determine second gradientinformation of the second data blocks. The at least one processor may befurther configured to direct the system to determine the relationshipbetween the input data and the tag data based on the first gradientinformation and the second gradient information.

In some embodiments, the determining the first set of training databased on the first image and the second image may include decomposingthe first image into a plurality of sets of first decomposition data,and decomposing the second image into a plurality of sets of seconddecomposition data. The number of the plurality of sets of firstdecomposition data may be equal to the number of the plurality of setsof second decomposition data. The determining the first set of trainingdata may further include generating a plurality of matching pairs ofdecomposition data based on the plurality of sets of first decompositiondata and the plurality of sets of second decomposition data. Each of theplurality of matching pairs of decomposition data may include a set offirst decomposition data and a corresponding set of second decompositiondata. The determining the first set of training data may further includedetermining the first set of training data based on one of the pluralityof matching pair of decomposition data.

In some embodiments, the decomposing of the first image may be performedaccording to a frequency of the at least of the first image or imagedata related to the first image. The decomposing of the second image maybe performed according to a frequency of the at least one of the secondimage or image data related to the second image.

In some embodiments, the decomposing of the first image may be performedaccording to at least one of a wavelet decomposition technique or aLaplacian decomposition technique. The decomposing of the second imagemay be performed according to at least one of the wavelet decompositiontechnique or the Laplacian decomposition technique.

In some embodiments, the plurality of sets of first decomposition datamay include a plurality of first decomposed images corresponding to aplurality of frequency bands. The plurality of sets of seconddecomposition data may include a plurality of second decomposed imagescorresponding to the plurality of frequency bands. To generate thetrained neural network model for scattering correction, the at least oneprocessor may be further configured to direct the system to determine arelationship between a first decomposed image corresponding to thefrequency band and a second decomposed image corresponding to thefrequency band for each of the plurality of frequency bands. The atleast one processor may be further configured to direct the system todetermine the trained neural network model for scattering correctionbased on the determined relationships for the plurality of frequencybands.

In another aspect of the present disclosure, a system is provided. Thesystem may include a storage device, and at least one processorconfigured to communicate with the storage device. The storage devicemay store a set of instructions. When executing the set of instructions,the at least one processor may be configured to direct the system toobtain a target image and one or more target imaging parameterscorresponding to the target image. The at least one processor may bealso configured to obtain one or more trained neural network models forscattering correction corresponding to one or more sets of imagingparameters. The at least one processor may be further configured toselect a target trained neural network model for scattering correctioncorresponding to the one or more target imaging parameters among the oneor more trained neural network models for scattering correction. The atleast one processor may be further configured to correct the targetimage using the target trained neural network model for scatteringcorrection.

In some embodiments, one of the one or more trained neural network modelfor scattering correction may be trained using training data. Thetraining data may include at least one first set of training data. Thefirst set of training data may be generated by a process for generatinga first set of training data. The process may include obtaining a firstimage corresponding to the one or more imaging parameters. The processmay also include obtaining a second image. The second image may includeless scattering noises than the first image. The process may furtherinclude determining the first set of training data based on the firstimage and the second image.

In some embodiments, to correct the target image, the at least oneprocessor may be further configured to direct the system to decomposethe target image into a plurality of decomposed target imagescorresponding to a plurality of frequency bands. The at least oneprocessor may be further configured to direct the system to correct theplurality of decomposed target images based on the target trained neuralnetwork model. The at least one processor may be further configured togenerate the corrected target image based on the plurality of correcteddecomposed target images.

In yet another aspect of the present disclosure, a method is provided.The method may be implemented on a computing device including a storagedevice and at least one processor. The method may include generating atrained neural network model for scattering correction corresponding toone or more imaging parameters. The trained neural network model may betrained using training data. The training data may include at least onefirst set of training data. The first set of training data may begenerated according to a process for generating the first set oftraining data. The process may include obtaining a first imagecorresponding to the one or more imaging parameters. The process mayalso include obtaining a second image. The second image may include lessscattering noises than the first image. The process may further includedetermining the first set of training data based on the first image andthe second image.

In yet another aspect of the present disclosure, a method is provided.The method may be implemented on a computing device including a storagedevice and at least one processor. The method may include obtaining atarget image and one or more target imaging parameters corresponding tothe target image. The method may further include obtaining one or moretrained neural network models for scattering correction corresponding toone or more sets of imaging parameters. The method may also includeselecting a target trained neural network model for scatteringcorrection corresponding to the one or more target imaging parametersamong the one or more trained neural network models for scatteringcorrection. The method may further include correcting the target imageusing the target trained neural network model for scattering correction.

In yet another aspect of the present disclosure, a non-transitorycomputer-readable storage medium is provided. The non-transitorycomputer-readable storage medium store instructions, when executed by atleast one processor of a system, cause the system to perform a method.The method may include generating a trained neural network model forscattering correction corresponding to one or more imaging parameters.The trained neural network model may be trained using training data. Thetraining data may include at least one first set of training data. Thefirst set of training data may be generated according to a process forgenerating the first set of training data. The process may includeobtaining a first image corresponding to the one or more imagingparameters. The process may also include obtaining a second image. Thesecond image may include less scattering noises than the first image.The process may further include determining the first set of trainingdata based on the first image and the second image.

In yet another aspect of the present disclosure, a non-transitorycomputer-readable storage medium is provided. The non-transitorycomputer-readable storage medium store instructions, when executed by atleast one processor of a system, cause the system to perform a method.The method may include obtaining a target image and one or more targetimaging parameters corresponding to the target image. The method mayfurther include obtaining one or more trained neural network models forscattering correction corresponding to one or more sets of imagingparameters. The method may also include selecting a target trainedneural network model for scattering correction corresponding to the oneor more target imaging parameters among the one or more trained neuralnetwork models for scattering correction. The method may further includecorrecting the target image using the target trained neural networkmodel for scattering correction.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing device may be implemented according to some embodiments ofthe present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIGS. 4A to 4C are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for generating atrained neural network model for scattering correction according to someembodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determininga first set of training data according to some embodiments of thepresent disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for generating atrained neural network model for scattering correction according to someembodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for determine arelationship between input data and tag data according to someembodiments of the present disclosure;

FIGS. 9A and 9B show exemplary convolutional neural network modelsaccording to some embodiments of the present disclosure;

FIG. 10 shows a schematic diagram of an exemplary process for training aconvolutional neural network model according to some embodiments of thepresent disclosure;

FIG. 11 shows an exemplary relationship between feature values and datablocks according to some embodiments of the present disclosure;

FIG. 12 is a schematic diagram illustrating image decompositionaccording to some embodiments of the present disclosure;

FIG. 13 is a flowchart illustrating an exemplary process for correctinga target image according to some embodiments of the present disclosure;

FIG. 14 shows a schematic diagram for correcting a target image based ona trained multi-scale convolutional neural network model according tosome embodiments of the present disclosure;

FIG. 15A shows an exemplary image according to some embodiments of thepresent disclosure; and

FIG. 15B shows an exemplary corrected image according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is to describe particular exampleembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” may be intended to include theplural forms as well, unless the context clearly indicates otherwise. Itwill be further understood that the terms “comprise,” “comprises,”and/or “comprising,” “include,” “includes,” and/or “including,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by another expression if theyachieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2 and/orthe central processing unit (CPU) 340 illustrated in FIG. 3) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM.

It will be further appreciated that hardware modules/units/blocks may beincluded in connected logic components, such as gates and flip-flops,and/or can be included of programmable units, such as programmable gatearrays or processors. The modules/units/blocks or computing devicefunctionality described herein may be implemented as softwaremodules/units/blocks, but may be represented in hardware or firmware. Ingeneral, the modules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

An aspect of the present disclosure relates to systems and methods forcorrecting scattering noises of images. In order to provide a moreefficient way of scattering correction, the systems and methods in thepresent disclosure may determine a trained neural network model forscattering correction corresponding to one or more certain imagingparameters. The trained neural network model corresponding to thecertain imaging parameter(s) may be used to correct images that areacquired under the same or substantially same imaging parameter(s). Tothis end, the systems and methods may generate the trained neuralnetwork model by training a preliminary neural network model usingtraining data. The training data may include at least one first set oftraining data. To generate a first set of training data, the systems andmethods may obtain a first image and a second image corresponding to thesame one or more imaging parameter(s). The second image may include lessscattering noises than the first image. The systems and methods maydetermine the first set of training data based on the first image andthe second image. Exemplary imaging parameters may include a tubevoltage, a tube current, a scanning time, an irradiation dose, a slicethickness of scanning, or the like, or any combination thereof.

The following description is provided to help better understand thegeneration of a trained neural network model. This is not intended tolimit the scope the present disclosure. For persons having ordinaryskills in the art, a certain amount of variations, changes, and/ormodifications may be deducted under the guidance of the presentdisclosure. Those variations, changes, and/or modifications do notdepart from the scope of the present disclosure.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. In someembodiments, the imaging system 100 may be a computed tomography (CT)system, a cone beam CT (CBCT) system, a computed radiography (CR)system, a digital radiography (DR) system, a digital subtractionangiography (DSA) system, a molti-modality system, or the like, or anycombination thereof. Examplary multi-modality systems may include acomputed tomography-positron emission tomography (CT-PET) system, acomputed tomography-magnetic resonance imaging (CT-MRI) system, etc.

As illustrated in FIG. 1, the imaging system 100 may include an imagingdevice 110, a network 120, one or more terminal(s) 130, a processingdevice 140, and a storage device 150. The components in the imagingsystem 100 may be connected in one or more of various ways. Merely byway of example, as illustrated in FIG. 1, the imaging device 110 may beconnected to the processing device 140 through the network 120. Asanother example, the imaging device 110 may be connected to theprocessing device 140 directly as indicated by the bi-directional arrowin dotted lines linking the imaging device 110 and the processing device140. As a further example, the storage device 150 may be connected tothe processing device 140 directly or through the network 120. As stilla further example, the terminal(s) 130 may be connected to theprocessing device 140 directly (as indicated by the bi-directional arrowin dotted lines linking the terminal 130 and the processing device 140)or through the network 120.

The imaging device 110 may be configured to scan a subject and generateimaging data used to generate one or more images relating to thesubject. The imaging device 110 may include a gantry 111, a detector112, a radiation source 113, and a scanning table 114. The detector 112and the radiation source 113 may be oppositely mounted on the gantry111. A subject may be placed on the scanning table 114 and moved into adetection tunnel of the imaging device 110 for scan. The subject may bea biological subject (e.g., a patient, an animal) or a non-biologicalsubject (e.g., a human-made subject). In the present disclosure,“object” and “subject” are used interchangeably.

The radiation source 113 may emit X-rays to toward the subject duringthe scan. The detector 112 may detect radiations (e.g., X-rays) emittedfrom the radiation source 113. In some embodiments, the detector 112 mayinclude a plurality of detector units. The detector units may include ascintillation detector (e.g., a cesium iodide detector) or a gasdetector. The detector units may be arranged in a single row or multiplerows. In some embodiments, the imaging device 110 may include one ormore components configured to prevent or reduce radiation scatteringsduring a scan. For example, the imaging device 110 may include a grid(e.g., a metal grid), a slit, a beam stop array (BSA), a beamattenuation grid (BAG), and/or any other component that may prevent orreduce radiation scatterings.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components in the imaging system 100 (e.g., theimaging device 110, the terminal 130, the processing device 140, or thestorage device 150) may communicate information and/or data with anothercomponent(s) of the imaging system 100 via the network 120. For example,the processing device 140 may obtain image data from the imaging device110 via the network 120. As another example, the processing device 140may obtain user instructions from the terminal(s) 130 via the network120.

In some embodiments, the network 120 may be any type of wired orwireless network, or a combination thereof. The network 120 may beand/or include a public network (e.g., the Internet), a private network(e.g., a local area network (LAN), a wide area network (WAN)), etc.), awired network (e.g., an Ethernet network), a wireless network (e.g., an802.11 network, a Wi-Fi network, etc.), a cellular network (e.g., a LongTerm Evolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 120 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network120 may include one or more network access points. For example, thenetwork 120 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the imaging system 100 may be connected to thenetwork 120 to exchange data and/or information.

The terminal(s) 130 include a mobile device 130-1, a tablet computer130-2, a laptop computer 130-3, or the like, or any combination thereof.In some embodiments, the mobile device 130-1 may include a smart homedevice, a wearable device, a smart mobile device, a virtual realitydevice, an augmented reality device, or the like, or any combinationthereof. In some embodiments, the smart home device may include a smartlighting device, a control device of an intelligent electricalapparatus, a smart monitoring device, a smart television, a smart videocamera, an interphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a smart bracelet, smartfootgear, a pair of smart glasses, a smart helmet, a smartwatch, smartclothing, a smart backpack, a smart accessory, or the like, or anycombination thereof. In some embodiments, the smart mobile device mayinclude a smartphone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include GoogleGlasses, an Oculus Rift, a Hololens, a Gear VR, etc.

In some embodiments, the terminal(s) 130 may remotely operate theimaging device 110. In some embodiments, the terminal(s) 130 may operatethe imaging device 110 via a wireless connection. In some embodiments,the terminal(s) 130 may receive information and/or instructions inputtedby a user, and send the received information and/or instructions to theimaging device 110 or to the processing device 140 via the network 120.In some embodiments, the terminal(s) 130 may receive data and/orinformation from the processing device 140. In some embodiments, theterminal(s) 130 may be part of the processing device 140. In someembodiments, the terminal(s) 130 may be omitted.

The processing device 140 may process data and/or information obtainedfrom the imaging device 110, the terminal 130, and/or the storage device150. For example, the processing device 140 may train a preliminaryneural network model using training data to generate a trained neuralnetwork model for scattering correction. In some embodiments, theprocessing device 140 may be a single server, or a server group. Theserver group may be centralized, or distributed. In some embodiments,the processing device 140 may be local or remote. For example, theprocessing device 140 may access information and/or data stored in theimaging device 110, the terminal 130, and/or the storage device 150 viathe network 120. As another example, the processing device 140 may bedirectly connected to the imaging device 110, the terminal 130, and/orthe storage device 150 to access stored information and/or data. In someembodiments, the processing device 140 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof. In some embodiments, the processing device 140 maybe implemented on a computing device 200 having one or more componentsillustrated in FIG. 2 in the present disclosure.

The storage device 150 may store data and/or instructions. In someembodiments, the storage device 150 may store data obtained from theterminal(s) 130 and/or the processing device 140. In some embodiments,the storage device 150 may store data and/or instructions that theprocessing device 140 may execute or use to perform exemplary methodsdescribed in the present disclosure. In some embodiments, the storagedevice 150 may include a mass storage device, removable storage device,a volatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. Exemplary mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. Exemplaryremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (PEROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 150 may be implemented on acloud platform. Merely by way of example, the cloud platform may includea private cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more components in the imagingsystem 100 (e.g., the processing device 140, the terminal(s) 130, etc.).One or more components in the imaging system 100 may access the data orinstructions stored in the storage device 150 via the network 120. Insome embodiments, the storage device 150 may be directly connected to orcommunicate with one or more components in the imaging system 100 (e.g.,the processing device 140, the terminal(s) 130, etc.). In someembodiments, the storage device 150 may be part of the processing device140.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device 200 on which theprocessing device 140 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2, the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 may processdata and/or image obtained from the imaging device 110, the terminal130, the storage device 150, and/or any other component in the imagingsystem 100. As another example, the processor 210 may determine a set oftraining data based on a first image and a second image of the samesubject. As yet another example, the processer 210 may train apreliminary neural network model using training data to generate atrained neural network model for scattering correction. As still anotherexample, the processor 210 may correct a target image based on a trainedneural network model for scattering correction.

In some embodiments, the processor 210 may perform instructions obtainedfrom the terminal(s) 130. In some embodiments, the processor 210 mayinclude one or more hardware processors, such as a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both process A and process B, it should be understood thatprocess A and process B may also be performed by two or more differentprocessors jointly or separately in the computing device 200 (e.g., afirst processor executes process A and a second processor executesprocess B, or the first and second processors jointly execute processesA and B).

The storage 220 may store data/information obtained from the imagingdevice 110, the terminal(s) 130, the storage device 150, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and theimaging device 110, the terminal(s) 130, or the storage device 150. Theconnection may be a wired connection, a wireless connection, orcombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobilenetwork (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof.In some embodiments, the communication port 240 may be a standardizedcommunication port, such as RS232, RS485, etc. In some embodiments, thecommunication port 240 may be a specially designed communication port.For example, the communication port 240 may be designed in accordancewith the digital imaging and communications in medicine (DICOM)protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 according to someembodiments of the present disclosure. As illustrated in FIG. 3, themobile device 300 may include a communication platform 310, a display320, a graphic processing unit (GPU) 330, a central processing unit(CPU) 340, an I/O 350, a memory 360, and a storage 390. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., iOS, Android, Windows Phone, etc.) and one or more applications380 may be loaded into the memory 360 from the storage 390 in order tobe executed by the CPU 340. The applications 380 may include a browseror any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information from theprocessing device 140. User interactions with the information stream maybe achieved via the I/O 350 and provided to the processing device 140and/or other components of the imaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to determine a trained neural network modelas described herein. A computer with user interface elements may be usedto implement a personal computer (PC) or another type of work station orterminal device, although a computer may also act as a server ifappropriately programmed. It is believed that those skilled in the artare familiar with the structure, programming and general operation ofsuch computer equipment and as a result the drawings should beself-explanatory.

FIGS. 4A to 4C are a block diagrams illustrating exemplary processingdevices 140-A, 140-B, and 140-C according to some embodiments of thepresent disclosure. In some embodiments, the processing device 140-A maybe configured to process information and/or data to determine a set oftraining data. The processing device 140-B may be configured to train apreliminary neural network model using training data to generate atrained neural network model for scattering correction. The processingdevice 140-C may be configured to correct an image based on a trainedneural network model for scattering correction. In some embodiments, theprocessing devices 140-A, 140-B, 140-C may respectively be implementedon a computing device 200 (e.g., the processor 210) illustrated in FIG.2 or a CPU 340 as illustrated in FIG. 3. Merely by way of example, theprocessing devices 140-A and 140-B may respectively be implemented on acomputing device 200, and the processing device 140-C may be implementedon a CPU 340 of a terminal device. Alternatively, at least two of theprocessing devices 140-A, 140-B, and 140-C may be implemented on a samecomputing device 200 or a same CPU 340. For example, the processingdevices 140-A and the processing device 140-B may be implemented on asame computing device 200.

The processing device 140-A may include an obtaining module 410, aprocessing module 420, and a determination module 430.

The obtaining module 410 may be configured to obtain information relatedto the imaging system 100. The information may include scan data, imagedata (one or more images), or the like. In some embodiments, theinformation may further be used to determine a set of training data. Forexample, the obtaining module 410 may obtain a first image and a secondimage of the same subject. The second image may include less scatteringnoises than the first image. The first and second images may be used todetermine a set of training data. In some embodiments, the obtainingmodule 410 may obtain the information related to the imaging system 100from one or more components of the imaging system 100, such as theimaging device 110, a storage device (e.g., the storage device 150).Additionally or alternatively, the obtaining module 410 may obtain theinformation from an external source via the network 120.

The processing module 420 may process an image. The processing of theimage may include an image rotation, an image flipping, an imagenormalization, an image enhancement, an image filtering, or the like, orany combination thereof. Merely by way of example, the processing module420 may rotate the image and then normalize the rotated image. Detailsregarding the image processing may be found elsewhere in the presentdisclosure (e.g., step 530 of the process 500 and the relevantdescriptions thereof).

The determination module 430 may determine a set of training data. Forexample, the determination module 430 may determine a set of trainingdata based on a (processed) first image and a (processed) second imageof the same subject. The (processed) second image may include lessscattering noises than the (processed) first image. The set of trainingdata may include input data and tag data. The tag data may also bereferred to as reference data or label data. The determination module430 may determine the input data based on the (processed) first image,and determine the tag data based on the (processed) second image. Insome embodiments, the determination module 430 may respectivelydecompose the (processed) first image and the (processed) second imageinto a plurality of sets of decomposition data. The determination module430 may also determine one or more sets of training data based on thesets of decomposition data. Details regarding the determination of a settraining data may be found elsewhere in the present disclosure (e.g.,step 550 of the process 500 and the relevant descriptions thereof).

The processing device 140-B may include a model generation module 440.The model generation module 440 may be configured to generate a trainedneural network model for scattering correction by training a preliminaryneural network model using training data. The training data may beacquired from one or more components in the imaging system 100 (e.g.,the storage device 150, the processing device 140-A), and/or an externalsource. For example, the training data may include one or more setstraining data generated by the processing device 140-A. In someembodiments, the model generation module 440 may input the training datainto the preliminary neural network model to determine one or more modelparameters. The model generation module 440 may further determine thetrained neural network model for scattering correction based on thepreliminary neural network model and the model parameter(s). Detailsregarding the generation of a trained neural network model may be foundelsewhere in the present disclosure (e.g., step 560 of the process 500and the process 700, and the relevant descriptions thereof).

The processing device 140-C may include an obtaining module 450, aselection module 460, and a correction module 470.

The obtaining module 450 may obtain information related to the imagingsystem 100. The information may include scan data, image data (one ormore images), or the like. For example, the obtaining module 450 mayobtain a target image to be corrected and one or more target imagingparameters under which the target image is generated. As anotherexample, the obtaining module 450 may obtain one or more trained neuralnetwork models for scattering correction. In some embodiments, theobtaining module 450 may obtain the information from an external sourceand/or one or more components of the imaging system 100, such as aprocessing device 140 (e.g., the processing device 140-B), a storagedevice (e.g., the storage device 150).

The selection module 460 may select a target trained neural networkmodel used to correct the target image among the plurality of trainedneural network models for scattering correction. In some embodiments,the trained neural network models may correspond to a plurality of setsof imaging parameters. The selection module 460 may select the targettrained neural network model based on differences between the targetimaging parameters and each of the sets of imaging parameters. Forexample, the trained neural network model whose corresponding set ofimaging parameters has the smallest difference with the target imagingparameters may be designated as the target trained neural network model.

The correction module 470 may correct an image based on a trained neuralnetwork model. For example, the correction module 470 may perform ascattering correction on the target image based on the target trainedneural network model for scattering correction. In some embodiments, thetarget trained neural network model may be a trained multi-scaleconvolutional neural network model for scattering correction. Thecorrection module 470 may decompose the target image into a plurality ofdecomposed target images, and correct each of the decomposed targetimages based on the multi-scale convolutional neural network model. Thecorrection module 470 may also generate the corrected target image basedon the plurality of corrected decomposed target images. Detailsregarding the correction of an image may be found elsewhere in thepresent disclosure (e.g., step 1350 of the process 1300 and the relevantdescriptions thereof).

The modules in the processing devices 140A, 140B, and the 140C may beconnected to or communicate with each other via a wired connection or awireless connection. The wired connection may include a metal cable, anoptical cable, a hybrid cable, or the like, or any combination thereof.The wireless connection may include a Local Area Network (LAN), a WideArea Network (WAN), a Bluetooth, a ZigBee, a Near Field Communication(NFC), or the like, or any combination thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

In some embodiments, two or more of the modules of a processing device140 (e.g., the processing device 140-A, the processing device 140-B,and/or the processing device 140C) may be combined into a single module,and any one of the modules may be divided into two or more units. Forexample, the model generation module 440 may be divided into two units.The first unit may be configured to acquire a preliminary neural networkmodel, and the second unit may be configured to determine one or moremodel parameters and determine a trained neural network model. In someembodiments, a processing device 140 (the processing device 140-A, theprocessing device 140-B, and/or the processing device 140C) may includeone or more additional modules. For example, the processing device 140Amay include a storage module (not shown) configured to store data. Insome embodiments, any two or more of the processing devices 140-A,140-B, and 140-C may be integrated to a single processing device 140 toperform the functions thereof. Merely by way of example, the processingdevice 140-A and the processing device 140-B may be integrated into aprocessing device 140. The integrated processing device 140 maydetermine one or more sets of training data, and train a preliminaryneural network model using the one or more determined sets of trainingdata and other sets of training data (if any).

FIG. 5 is a flowchart illustrating an exemplary process for generating atrained neural network model for scattering correction according to someembodiments of the present disclosure. The process 500 may beimplemented in the imaging system 100 illustrated in FIG. 1. Forexample, the process 500 may be stored in the storage device 150 and/orthe storage 220 in the form of instructions (e.g., an application), andinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 illustrated in FIG. 2, or one or more modules in theprocessing device 140-A illustrated in FIG. 4A, and/or in the processingdevice 140-B illustrated in FIG. 4B). The operations of the illustratedprocess presented below are intended to be illustrative. In someembodiments, the process 500 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 500 as illustrated in FIG. 5 and described below is notintended to be limiting.

In 510, the obtaining module 410 may obtain a first image correspondingto one or more imaging parameters. In some embodiments, “obtaining afirst image” may refer to obtaining the first image and/or image datarelated to the first image. The image date related to the first imagemay include, for example, pixel values of one or more pixels or voxelvalues of one or more voxels in the first image.

In some embodiments, the first image may be simulated under the one ormore image parameters by a Monte-Carlo technique. Alternatively, thefirst image may be generated according to first scan data acquired by animaging device (e.g., the imaging device 110) under one or more imagingparameters. In some embodiments, the one or more imaging parameters mayinclude a tube voltage, a tube current, a scanning time, an irradiationdose, a slice thickness of scanning, or the like, or any combinationthereof. In some embodiments, the one or more imaging parameters may bedetermined based on a scanning plan. Different scanning plans mayinclude different imaging parameters depending on different diagnosticneeds.

The imaging device 110 may perform a first scan on a subject accordingto the one or more imaging parameters to collect the first scan data.The subject may be a biological subject (e.g., a human, an animal) or anon-biological subject (e.g., a phantom). In some embodiments, theimaging device 110 may be configured without a grid during the firstscan. The grid may be configured to reduce or eliminate radiationscattering. The first image may be generated according to the first scandata, which may include scattering noises. In some embodiments, theobtaining module 410 may obtain the first image from one or morecomponents of the imaging system 100, such as the imaging device 110, astorage device (e.g., the storage device 150). Alternatively, theobtaining module 410 may obtain the first image from an external sourcevia the network 120.

In 520, the obtaining module 410 may obtain a second image. The secondimage may include less scattering noises than the first image. In someembodiments, “obtaining a second image” may refer to obtaining thesecond image and/or image data related to the second image. The imagedate related to the second image may include, for example, pixel valuesof one or more pixels or voxel values of one or more voxels in thesecond image

In some embodiments, the second image may be generated according tosecond scan data acquired by the imaging device 110. For example, theimaging device 110 may be configured with the grid and perform a secondscan on the subject under the same imaging parameter(s) as the firstscan to obtain the second scan data. The grid may reduce or eliminateradiation scattering, and the second image may include less scatteringnoises than the first image. As another example, the second image may begenerated based on the first image by removing at least some of thescattering noises of the first image. For example, a scatteringcorrection may be performed on the first image to generate the secondimage. Exemplary scattering correction techniques may include an imagesmoothing technique, an image enhancement technique, a Monte Carlosimulation technique, a single scatter simulation technique, a dualenergy-window technique, a tail fitting technique, or the like, or anycombination thereof. As yet another example, an image may be generatedaccording to the second scan data. The second image may be determinedbased on the image by removing at least some of the scattering noises ofthe image.

In some embodiments, the first and the second images may be simulatedimages generated according to a Monte-Carlo technique. The first imagemay be simulated to include more scattering noises than the secondimage. Merely by way of example, the first image may be an imageincluding scattering noises simulated by the Monte-Carlo technique. Thesecond image may be an image without scattering noises simulated by theMonte-Carlo technique. The simulations of the first and second imagesmay be performed under the same one or more imaging parameters asdescribed in connection with operation 510.

In some embodiments, the obtaining module 410 may obtain the secondimage from one or more components of the imaging system 100, such as theimaging device 110, a storage device (e.g., the storage device 150).Alternatively, the obtaining module 410 may obtain the second image froman external source via the network 120.

In 530, the processing module 420 may process the first image. Theprocessing of the first image may include an image rotation, an imageflipping, an image normalization, an image enhancement, an imagefiltering, or the like, or any combination thereof. For example, theprocessing module 420 may flip the first image horizontally, vertically,or in any other direction. Alternatively or additionally, the processingmodule 420 may rotate the first image by an angel. The first image maybe rotated clockwise or counterclockwise. The angle may be any valuebetween 0 and 360°. In some embodiments, the processing module 420 maynormalize the first image with respect to its range of pixel valuesaccording to a normalization technique. Exemplary normalizationtechniques may include a min-max normalization algorithm, a Z-scorestandardization technique, a linear normalization technique, etc. Insome embodiments, the pixel value of each pixel in the first image maybe normalized according to Equation (1) as below:

$\begin{matrix}{{N_{i} = \frac{P_{i} - I_{\min}}{I_{\max} - I_{\min}}},} & (1)\end{matrix}$where I_(max) refers to the maximum pixel value of the first image;I_(min) refers to the minimum pixel value of the first image; i refersto a pixel in the first image; P_(i) refers to the pixel value of thepixel i; and N_(i) refers to a normalized pixel value of the pixel i.

Alternatively, the pixel value of each pixel in the first image may benormalized according to Equation (2) as below:

$\begin{matrix}{{N_{i} = \frac{P_{i} - I_{a}}{{nI}_{\nu}}},} & (2)\end{matrix}$where I_(a) refers to an average pixel value of the first image; I_(v)refers to a variance of pixel values of the first image; n refers to acoefficient; i refers to a pixel in the first image; P_(i) refers to thepixel value of the pixel i; and N_(i) refers to the normalized value ofthe pixel i. The coefficient n may have any positive value. For example,n may be 3.

In some embodiments, the processing module 420 may perform a pluralityof processing operations on the first image. For example, the processingmodule 420 may transform the first image by flipping and/or rotating thefirst image, and then normalize the transformed first image. As anotherexample, the processing module 420 may normalize the first image, andthen transform the normalized first image by flipping and/or rotatingthe normalized first image. In some embodiments, the processing module420 may flip the first image in different directions, and/or rotate thefirst image by different angles to generate a plurality of transformedfirst images. Additionally or alternatively, the processing module 420may normalize the transformed first images.

In 540, the processing module 420 may process the second image. Theprocessing of the second image may include an image rotation, an imageflipping, an image normalization, an image enhancement, an imagefiltering, or the like, or any combination thereof. The second image maybe processed in the same or substantially same manner as the firstimage. For example, the second image may be rotated by the same angle asthe first image, and then be normalized by the same way as the firstimage. Details regarding the image processing (e.g., the image rotation,the image normalization) may be found elsewhere in the presentdisclosure (e.g., step 530 and the relevant descriptions thereof).

In 550, the determination module 430 may determine a first set oftraining data based on the processed first image and the processedsecond image.

In some embodiments, the first set of training data may include inputdata and tag data. The input data may be determined based on theprocessed first image, and the tag data may be determined based on theprocessed second image. For example, the determination module 430 mayselect a first sub-area from the processed first image, and designatethe image data corresponding to the first sub-area (e.g., the pixelvalues of the pixel in the first sub-area) as the input data. Thedetermination module 430 may select a second sub-area corresponding tothe first sub-area from the processed second image. The determinationmodule 430 may also designate the image data corresponding to the secondsub-area as the tag data. The second sub-area corresponding to the firstsub-area may have the same position in the processed second image asthat of the first sub-area in the processed first image.

In some embodiments, the first sub-area may be selected in a random wayor according to a sampling window. The sampling window may have any sizeand be located at any position in the first image. In some embodiments,the number of pixels in a side of the sampling window may range from 40to 100. The size of the sampling window may range from 40×40 to 100×100.Merely by way of example, the size of the first sub-area may be 50×50,70×70, or the like. After the first sub-area is selected, thedetermination module 430 may select the second sub-area locating at thesame position as the first sub-area from the second image.

In some embodiments, steps 530 and 540 may be omitted. The determinationmodule 430 may determine the first set of training data based on thefirst image and the second image. The first set of training data mayinclude input data and tag data. The input data may be determined basedon the first image, and the tag data may be determined based on thesecond image. The determination of the input data and the tag data basedon the first and second images may be similar to that based on theprocessed first and processed second images, and the descriptionsthereof are not repeated.

In some embodiments, the determination module 430 may decompose the(processed) first image into a plurality of sets of first decompositiondata, and decompose the (processed) second image into a plurality ofsets of second decomposition data. The determination module 430 may alsodetermine the first set of training data based on the sets of first andsecond decomposition data. In some embodiments, the first set oftraining data determined based on the sets of first and seconddecomposition data may correspond to a specific frequency band. Detailsregarding the determination of the first set of training data based onthe first and second decomposition data may be found elsewhere in thepresent disclosure (e.g., FIG. 6, and the relevant descriptionsthereof).

In 560, the model generation module 440 may generate a trained neuralnetwork model for scattering correction corresponding to the one or moreimaging parameters by training a preliminary neural network model usingtraining data. The training data may include at least one of the firstset of training data.

In some embodiments, the training data may include a plurality of firstsets of training data. Different first sets of training data may begenerated in the same way or different ways. For example, in 530, theprocessing module 420 may process (e.g., rotate, flip, and/or normalize)the first image to generate a plurality of processed first images. In540, the processing module 420 may process (e.g., rotate, flip, and/ornormalize) the second image to generate a plurality of processed secondimages. The determination module 430 may determine a plurality of firstsets of training data by repeating step 550 for each processed firstimage and the corresponding processed second image. As another example,in step 550, the determination module 430 may select a plurality offirst sub-areas and corresponding second sub-areas from the first imageand second image respectively. The determination module 430 may furtherdetermine a plurality of first sets of training data based on the firstsub-areas and the second sub-areas. As yet another example, steps 510 to550 may be performed on a plurality of first images and a plurality ofsecond images to generate a plurality of first sets of training data.

In some embodiments, the training data may include one or more firstsets of training data that have a specific characteristic. For example,the training data may include one or more first sets of training datathat correspond to a frequency band. The trained neural network modelfor scattering correction generated based on the training data may beused to correct an image (or a portion thereof) that corresponds to thefrequency band. In some embodiments, the training data may furtherinclude one or more sets of training data suitable for training a neuralnetwork model for scattering correction. The sets of training data maybe generated in a similar or different way as the first set of trainingdata.

In some embodiments, the preliminary neural network model may include aconvolutional neural network model, a perceptron neural network model, adeep trust network model, a stack self-coding network model, a recurrentneural network model, or the like, or any combination thereof. Thepreliminary neural network model may include one or more preliminaryparameters. The model generation module 440 may input the training datainto the preliminary neural network model to adjust the preliminaryparameters. The adjusted parameters may be referred to as modelparameters. The trained neural network model for scattering correctionmay be determined based on the model parameters and the preliminaryneural network model. Details regarding the generation of a trainedneural network model for scattering correction may be found elsewhere ofthe present disclosure (e.g., FIG. 7, and the relevant descriptionsthereof).

It should be noted that the above description of the process 500 isprovided for the purposes of illustration, and is not intended to limitthe scope of the present disclosure. For persons having ordinary skillsin the art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, steps 530 and 540 may be omitted. The determination module430 may determine the first set training data based on the first imageand the second image. As another example, step 530 may be performedbefore step 540, or steps 530 and 540 may be performed simultaneously.As yet another example, step 560 may be omitted. In some embodiments,the process 500 may repeatedly be performed to generate a plurality oftrained neural network model for scattering correction corresponding toa plurality of sets of imaging parameters. Different trained neuralnetwork models corresponding to different sets of imaging parameters maybe used to correct images acquired under different sets of imagingparameters (will be described in FIG. 13).

FIG. 6 is a flowchart illustrating an exemplary process for determininga first set of training data according to some embodiments of thepresent disclosure. The process 600 may be implemented in the imagingsystem 100 illustrated in FIG. 1. For example, the process 600 may bestored in the storage device 150 and/or the storage 220 in the form ofinstructions (e.g., an application), and invoked and/or executed by theprocessing device 140 (e.g., the processor 210 illustrated in FIG. 2, orone or more modules in the processing device 140-A illustrated in FIG.4A). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 600 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 600 as illustrated inFIG. 6 and described below is not intended to be limiting. In someembodiments, the steps 530 and 540 of process 500 may be omitted. Thestep 550 of the process 500 may be performed according to the process600.

In 610, the determination module 430 may decompose the first image intoa plurality of sets of first decomposition data. The number of the setsof first decomposition data may be any positive value, for example, 4,7, or 10. In some embodiments, the number of the sets of firstdecomposition data may range from 4 to 8. In some embodiments, the setsof first decomposition data may include a plurality of first decomposedimages.

In some embodiments, the first image may be decomposed according to afrequency of at least one of the first image or image data (e.g., pixelvalues or voxel values) related to the first image. The sets of firstdecomposition data may correspond to a plurality of frequency bands. Insome embodiments, the determination module 430 may decompose the firstimage according to an image decomposition technique. Exemplary imagedecomposition techniques may include a wavelet decomposition technique,a Laplacian decomposition technique, a Gaussian decomposition technique,a singular value decomposition technique, or the like, or anycombination thereof. Merely by way of example, the first image may bedecomposed into seven sets of first decomposition data corresponding toseven frequency bands according to the Laplacian decomposition technique(which will be described in detail in connection with FIG. 12).

In 620, the processing engine 130 may decompose the second image into aplurality of sets of second decomposition data. The second image may bedecomposed according to a frequency of at least one of the second imageor image data (e.g., pixel values or voxel values) related to the secondimage. The second image may be decomposed in the same or substantiallysame manner as the first image. The sets of second decomposition datamay correspond to the same or substantially same frequency bands as thesets of first decomposition data. The number of the sets of seconddecomposition data may be equal to the number of sets of the firstdecomposition data. In some embodiments, the sets of seconddecomposition data may include a plurality of second decomposed images.

In 630, the determination module 430 may generate a plurality ofmatching pairs of decomposition data based on the sets of first andsecond decomposition data. Each of the matching pairs may include a setof first decomposition data and a corresponding set of seconddecomposition data. In some embodiments, as described in connection withsteps 610 and 620, the sets of first and second decomposition data maycorrespond to a plurality of frequency bands respectively. The set offirst decomposition data and its corresponding set of seconddecomposition data may correspond to the same frequency band.

In 640, the determination module 430 may determine the first set oftraining data based on one of the matching pairs of decomposition data.The first set of training data may include input data and tag data asdescribed in connection with step 550 of the process 500. For a matchingpair of decomposition data, the input data may be determined based onthe set of first decomposition data (e.g., a first decomposed image),and the tag data may be determined based on the corresponding set ofsecond decomposition data (e.g., a second decomposed image). Thedetermination of the input data based on the set of first decompositiondata may be similar to that based on the (processed) first image. Thedetermination of the tag data based on the set of second decompositiondata may be similar to that based on the (processed) second image. Forexample, the determination module 430 may select a third sub-area fromthe set of first decomposition data (e.g., a first decomposed image).The determination module 430 may also designate the image datacorresponding to the third sub-area (e.g., the pixel values of the pixelin the third sub-area) as the input data. The determination module 430may further select a fourth sub-area corresponding to the third sub-areafrom the set of second decomposition data (e.g., a second decomposedimage) and designate the image data corresponding to the fourth sub-areaas the tag data. The fourth sub-area corresponding to the third sub-areamay have the same position in the set of second decomposition data asthat of the third sub-area in the set of first decomposition data.Details regarding the determination of input data and tag data may befound elsewhere in the present disclosure (e.g., step 550 of the process500, and the relevant descriptions thereof).

In some embodiments, the sets of first and second decomposition data ofa matching pair may correspond to a frequency band. The first set oftraining data generated based on the matching pair may be used togenerate a trained neural network model corresponding to the frequencyband by performing step 560. The trained neural network modelcorresponding to the frequency band may further be used to correct animage (or a portion thereof) that corresponds to the frequency band(which will be described in detail in connection with FIG. 13).

In some embodiments, for each of the matching pairs of decompositiondata, the determination module 430 may determine a first set of trainingdata. Accordingly, a plurality of first sets of training datacorresponding to different frequency bands may be determined in 640. Thefirst sets of training data may further be used to generate a pluralityof trained neural network models for scattering correction correspondingto the different frequency bands. Alternatively, the first sets oftraining data may be used to generate a trained multi-scaleconvolutional neural network model for scattering correction. Detailsregarding the trained multi-scale convolutional neural network model maybe found elsewhere in the present disclosure (e.g., FIGS. 12 and 14, andthe relevant descriptions thereof). In some embodiments, a processsimilar to the process 600 may be performed on a processed first imageand a processed second image to achieve step 550 of the process 500. Forexample, the determination module 430 may respectively decompose theprocessed first image and the processed second image into sets ofdecomposition data. The determination module 430 may further determine aplurality of matching pairs of decomposition data based on the sets ofdecomposition data, and determine the first set of training dataaccordingly.

FIG. 7 is a flowchart illustrating an exemplary process for generating atrained neural network model for scattering correction according to someembodiments of the present disclosure. The process 700 may beimplemented in the imaging system 100 illustrated in FIG. 1. Forexample, the process 700 may be stored in the storage device 150 and/orthe storage 220 in the form of instructions (e.g., an application), andinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 illustrated in FIG. 2, or one or more modules in theprocessing device 140-B illustrated in FIG. 4B). The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 700 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 700 as illustrated in FIG. 7 and described below is notintended to be limiting. In some embodiments, the process 700 may be anembodiment of step 560 of the process 500.

In 710, the model generation module 440 may acquire a preliminary neuralnetwork model. The preliminary neural network model may include one ormore preliminary parameters. The one or more preliminary parameters maybe adjusted during the training process of the preliminary neuralnetwork model.

In some embodiments, exemplary preliminary neural network models mayinclude a convolutional neural network model (e.g., a multi-scaleconvolutional neural network model, a super-resolution convolutionalneural network model, a denoising convolutional neural network model), aperceptron neural network model, a deep trust network model, a stackself-coding network model, a recurrent neural network model (e.g., along short term memory (LSTM) neural network model, a hierarchicalrecurrent neural network model, a bi-direction recurrent neural networkmodel, a second-order recurrent neural network model, a fully recurrentnetwork model, an echo state network model, a multiple timescalesrecurrent neural network (MTRNN) model), or the like, or any combinationthereof. In some embodiments, the preliminary neural network model maybe a convolutional neural network model that includes a convolutionlayer, an activation layer, and a cost layer. Details regarding theconvolutional neural network model may be found elsewhere in the presentdisclosure (e.g., FIGS. 9 to 11, and the relevant descriptions thereof).

In 720, the model generation module 440 may determine one or more modelparameters by inputting the training data into the preliminary neuralnetwork model. The training data may correspond to a set of one or moreimaging parameters under which images (e.g., the first image, the secondimage described in FIG. 5) may be generated. The training data mayinclude input data and tag data as described in connection with steps550 and 640.

In some embodiments, the input data may be inputted into the preliminaryneural network model to generate an actual output. The tag data may beconsidered as a desired output. The model generation module 440 maycompare the actual output with the desired output to determine a lossfunction. The loss function may measure a difference between the actualoutput and the desired output (i.e., the tag data). During the trainingof the preliminary neural network model, the model generation module 440may adjust the one or more preliminary parameters to minimize the lossfunction. In some embodiments, the loss function and the preliminaryparameters may be updated iteratively in order to obtain a minimizedloss function. The iteration to minimize the loss function may beterminated until a termination condition is satisfied. An exemplarytermination condition is that an updated loss function with the updatedparameters obtained in an iteration is less than a predeterminedthreshold. The predetermined threshold may be set manually or determinedbased on various factors including, such as the accuracy of the trainedneural network model, etc. Other exemplary termination conditionsinclude that a certain iteration count of iterations are performed, thatthe loss function converges such that the differences of the values ofthe updated loss function obtained in consecutive iterations are withina threshold, etc.

After the loss function is minimized, the one or more newly adjustedpreliminary parameters may be designated as the one or more modelparameters of the trained neural network model.

In some embodiments, the one or more model parameters may include arelationship between the input data and the tag data. In someembodiments, the relationship between the input data and the tag datamay be a mapping function. Details regarding the determination of therelationship between the input data and the tag data may be foundelsewhere in the present disclosure (e.g., FIG. 8, and the relevantdescriptions thereof).

In 730, the model generation module 440 may determine the trained neuralnetwork model based on the one or more model parameters and thepreliminary neural network model. For example, the one or more modelparameters may be combined with the preliminary neural network model togenerate the trained neural network model. The trained neural networkmodel may be used for scattering correction. In some embodiments, thetraining data may correspond to one or more imaging parameters. Thetrained neural network model may be used to correct an image generatedunder the same or substantially same imaging parameters as the trainingdata.

In some embodiments, the preliminary neural network model may be amulti-scale convolutional neural network model that includes a pluralityof sub-convolutional neural network models. The training data of themulti-scale convolutional neural network model may include a pluralityof sets of training data that correspond to a plurality of frequencybands. Steps 720 and 730 may be performed for each sub-convolutionalneural network model based on a set of training data to generate atrained sub-convolutional neural network model. A trained multi-scaleconvolutional neural network model (e.g., the trained multi-scaleconvolutional neural network model 1210) may further be determined bycombining the trained sub-convolutional neural network models. Detailsregarding the training of a multi-scale convolutional neural networkmodel may be found elsewhere in the present disclosure (e.g., FIG. 12and the relevant descriptions thereof).

FIG. 8 is a flowchart illustrating an exemplary process for determininga relationship between input data and tag data according to someembodiments of the present disclosure. The process 800 may beimplemented in the imaging system 100 illustrated in FIG. 1. Forexample, the process 800 may be stored in the storage device 150 and/orthe storage 220 in the form of instructions (e.g., an application), andinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 illustrated in FIG. 2, or one or more modules in theprocessing device 140-B illustrated in FIG. 4B). The operations of theillustrated process presented below are intended to be illustrative. Insome embodiments, the process 800 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 800 as illustrated in FIG. 8 and described below is notintended to be limiting. In some embodiments, the process 800 may be anembodiment of step 720 of the process 7000.

In 810, the model generation module 440 may divide input data into aplurality of first data blocks. The input data may be determined basedon a (processed) first image and/or a first decomposed image asdescribed elsewhere in this disclosure (e.g., steps 550 and 640, and therelevant descriptions). The input data may include a plurality of pixelvalues of pixels in the (processed) first image and/or the firstdecomposed image. In some embodiments, the input data may be evenlydivided or unevenly divided. The sizes of different first data blocksmay be the same or different.

In 820, the model generation module 440 may divide tag data into aplurality of second data blocks corresponding to the plurality of firstdata blocks. The tag data may be determined based on a (processed)second image and/or a second decomposed image as described elsewhere inthis disclosure (e.g., step 550 of the process 500 and step 640 of theprocess 600, and the relevant descriptions). The tag data may include aplurality of pixel values of pixels in the (processed) second imageand/or the second decomposed image. The tag data may be divided in thesame or substantially same manner as the input data. The number of thesecond data blocks may be equal to the number of the first data blocks.The size of a second data block may be equal to the corresponding firstdata block.

In 830, the model generation module 440 may determine first gradientinformation (also referred to as a first gradient structure) of thefirst data blocks. The first gradient information of the first datablocks may include first gradient information of each first data block.The first gradient information of a first data block may includeX-direction gradient information and Y-direction gradient information ofeach pixel in the first data block. Merely by way of example, for apixel in a first data block, the model generation module 440 mayrespectively determine X-direction gradient information and Y-directiongradient information of the pixel according to Equation (3) and Equation(4) as below:

$\begin{matrix}{{G_{X}\left( {i,j} \right)} = \left\{ {\begin{matrix}{0,} & {i = 0} \\{{{I\left( {i,j} \right)} - {I\left( {{i - 1},j} \right)}},} & {i \geq 1}\end{matrix},} \right.} & (3) \\{{G_{Y}\left( {i,j} \right)} = \left\{ {\begin{matrix}{0,} & {j = 0} \\{{{I\left( {i,j} \right)} - {I\left( {i,{j - 1}} \right)}},} & {j \geq 1}\end{matrix},} \right.} & (4)\end{matrix}$where i refers to an abscissa of the pixel; j refers to an ordinate ofthe pixel; I refers to the pixel value (e.g., the grey value) of thepixel; G_(X) refers to the X-direction gradient information of thepixel; and G_(Y) refers to the Y-direction gradient information of thepixel. The i and j may both be positive.

The first gradient information for different pixels may be the same ordifferent. For example, the first gradient information of a pixel in afirst data block located at the edge of the (processed) first image maybe greater than that of a pixel in the first data block located at thecenter of the (processed) first image.

In 840, the model generation module 440 may determine second gradientinformation (also referred to as a second gradient structure) of thesecond data blocks. The second gradient information of the first datablocks may include second gradient information of each second datablock. The second gradient information of a second data block mayinclude X-direction gradient information and Y-direction gradientinformation of each pixel in the second data block. The second gradientinformation of the second data blocks may be determined in the same orsubstantially same manner as the first gradient information.

In 850, the model generation module 440 may determine a relationshipbetween the input data and the tag data. The relationship between theinput data and the tag data may refer to a relationship between thefirst gradient information of the first data blocks and the secondgradient information of the second data blocks. In some embodiments, therelationship may be expressed by a map function between the firstgradient information and the second gradient information.

In some embodiments, the relationship between the input data and the tagdata may be used to transform the first gradient information of thefirst data blocks. The transformed first gradient information may beregarded as an actual output generated by a (trained) neural networkmodel with the input data as the input to the (trained) neural networkmodel. In some embodiments, the transformed first gradient informationmay further be transformed into image data. Exemplary techniques totransform gradient information into image data may be found in, forexample, a literature entitled “DIRECT ANALYTICAL METHODS FOR SOLVINGPOISSON EQUATIONS IN COMPUTER VISION PROBLEMS” published on “IEEETransactions on Pattern Analysis and Machine Intelligence” on 1990, thecontents of which are hereby incorporated by reference.

In some embodiments, the input data and the tag data may respectively bedetermined based on a first and second decomposed image that correspondsto a frequency band as described in connection with step 640. Therelationship between the input data and the tag data may be regarded asa relationship between the first decomposed image and the seconddecomposed image.

In some embodiments, the model generation module 440 may determinegradient information (e.g., X-direction gradient information and/orY-direction gradient information) of each pixel in the input data andthe tag data. The model generation module 440 may divide the input datainto the first data blocks, and divide the tag data into the second datablocks. The gradient information of the pixel(s) in a first data blockmay be designated as first gradient information of the first data block.The gradient information of the pixel(s) in a second data block may bedesignated as second gradient information of the second data block.

FIGS. 9A and 9B show two exemplary convolutional neural network models900A and 900B according to some embodiments of the present disclosure.In some embodiments, the convolutional neural network models 900A and900B may be embodiments of a preliminary neural network model. Theconvolutional neural network models 900A and/or 900B may be trained togenerate a trained neural network model for scattering correction asdescribed elsewhere in this disclosure (e.g., FIGS. 5 and 7, and therelevant descriptions).

As shown in FIGS. 9A and 9B, the convolutional neural network models900A and 900B may respectively include one or more convolutional layers,one or more activation layers, and a cost layer. Each convolutionallayer other than the last convolutional layer may be followed by anactivation layer. The last convolutional layer may be followed by thecost layer. In some embodiments, the number of the convolutional layersmay be within a range of 5 to 13. For example, the convolutional neuralnetwork model 900A may include five convolutional layers, fouractivation layers, and one cost layer. As another example, theconvolutional neural network model 900A may include nine convolutionallayers, eight activation layers, and one cost layer. The kernel size ofa convolutional layer may be within a range of 3*3 to 11*11. Forexample, the kernel size of a convolutional layer may be 3*3. Differentconvolution layers may have the same kernel size or different kernelsizes. It should be noted that the above-described structure of theconvolutional neural network model 900A and/or 900B is merely providedfor the purposes of illustration, and not intended to limit the scope ofthe present disclosure. The convolutional neural network model 900Aand/or 900B may include any number of convolutional layers andactivation layers. The kernel size of a convolutional layer may be anypositive value.

During a training process of the convolutional neural network model 900Aor 900B, the input data (e.g., the one or more first data blocks, thefirst gradient information described in FIG. 8) may be inputted into thefirst convolutional layer and the first activation layer, and aplurality of feature maps (or feature data) may be generated. For theconvolutional layer(s) other than the first or last ones, the input mayinclude a plurality of feature maps (or feature data) generated by theprevious convolutional layer, and the output may include a plurality offeature maps (or feature data. For the last convolutional layer, theinput may include a plurality of feature images (or feature data)generated by the previous convolution layer, and the output may includea residual image (or residual data). In some embodiments, 64 featuremaps may be generated by the first convolutional layer. The number offeature maps inputted in and/or outputted by a convolutional layer maybe 64. In some embodiments, a feature map may be also referred to as aconvolutional image.

An activation layer may be represented by an activation function.Exemplary activation functions may include a rectified linear unit(ReLU), an exponential linear unit (ELU), a leaky ReLU, a parameterizedReLU (PReLU), a randomized ReLU (RReLU), a noisy ReLU, or the like, orany combination thereof. Different activation layers may adopt the sameactivation function or different activation functions.

The cost layer may be represented by a loss function that measures asimilarity (or a difference) between the tag data and an actual output.The actual output may be generated by inputting the input data into theconvolutional neural network model 900A or 900B. The actual output mayalso be referred to as reconstructed data (will be described in FIG.10). In some embodiments, when the loss function is equal to or lessthan a threshold, the training may be terminated since the similaritybetween the actual output and the tag data reaches a desired level.Exemplary loss functions may include a mean-square error (MSE) function,a mean absolute percent error (MAPE), a root mean square error (RMSE), across entropy function, a norm, etc. The norm may be, for example, a1-norm, a weighted 1-norm, or a weighted 2-norm of the actual output andthe tag data. Merely by way of example, the loss function may be the MSEfunction expressed by Equation (5) as below:

$\begin{matrix}{{{Loss} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{{{R\left( X_{i} \right)} - c_{i}}}^{2}}}},} & (5)\end{matrix}$where X_(i) refers to the i^(th) first data block of the input data;R(X_(i)) refers to an actual output of the i^(th) first data block X_(i)based on the convolutional neural network model 900A or 900B; c_(i)refers to the i^(th) second data block of the tag data; and N refers tothe number of the first or second data blocks.

In some embodiments, as illustrated in FIG. 9A, the output of the lastconvolution layer may be inputted into the cost layer to determine thecost function. As illustrated in FIG. 9B, the output of the lastconvolution layer may be added with the input data, and the sum of theinput data and the output of the last convolution layer may be inputtedinto the cost layer to determine the cost function.

FIG. 10 shows a schematic diagram of an exemplary process for training aconvolutional neural network model according to some embodiments of thepresent disclosure. The convolutional neural network model 1000 to betrained may be a super-resolution convolutional neural network (SRCNN)model. As illustrated in FIG. 10, the convolutional neural network model1000 includes three convolutional layers, two activation layers, and acost layer. The convolutional layers are denoted as the Conv 1, the Conv2, and the Conv 3. The activation layers are denoted as an activationfunction ReLU. The cost layer is denoted as a loss function MSE. Thetraining process of the convolutional neural network model 1000 mayinclude four stages, including feature extraction, non-linear mapping,reconstruction, and optimization stages.

During the feature extraction stage, the input data (e.g., the one ormore first data blocks, the first gradient information described in FIG.8) may be input into the convolutional layer Conv 1, during which thefeatures of the input data may be extracted. Then the activationfunction may perform a non-linear operation on the outputted featuresfrom the convolutional layer Conv 1. Exemplary techniques of performinga non-linear operation based on an activation function may be found in,for example, a literature entitled “RECTIFIED LINEAR UNITS IMPROVERESTRICTED BOLTZMANN MACHINES” published on “Proceedings of the 27thInternational Conference on Machine Learning” on 2010, the contents ofwhich are hereby incorporated by reference. Merely by way of example,for a first data block of the input data, the feature(s) may beextracted according to Equation (6) as below:F ₁(X _(i))=max(0,W ₁ *X _(i) +B ₁),  (6)wherein X_(i) refers to the i^(th) first data block of the input data;W₁ refers to a first filter; B₁ refers to a first deviation; * refers toa convolution operation; and F₁(X_(i)) refers to the feature value(s)extracted from the i^(th) first data block X_(i). The size of the firstfilter W₁ may be equal to c×f₁×f₁ in which c refers to the number ofchannels in the input data and f₁ refers to the spatial size of thefirst filter(s). An exemplary relationship between the feature valuesF₁(X_(i)) and the first data block X_(i) is shown in FIG. 11. Theabscissa represents first data block(s), and the ordinate representsextracted feature value(s) of the first data block(s). As shown in FIG.11, the feature value(s) may be equal to or larger than zero.

In some embodiments, a plurality of first filters may be used in thefeature extraction to extract various features. For example, n₁ firstfilters may be used to extract n₁ features of each first data block. Theextracted n₁ features of a first data block may be expressed by an₁-dimensional feature vector. In the non-linear mapping part of thetraining process, the n₁-dimensional feature vector may be inputted intothe convolutional layer Conv 2, after which a convolution operation (bythe convolutional layer Conv 2) and an activation operation (by theactivation function ReLU) may be performed on the n₁-dimensional featurevector. Merely by way of example, for a first data block of the inputdata, the n₁-dimensional feature vector may be processed according toEquation (7) as below:F ₂(X _(i))=max(0,W ₂*(X _(i))+B ₂),  (7)wherein W₂ refers to a second filter; B₂ refers to a second deviation;and F₂(X_(i)) refers to the feature value(s) of the first data blockX_(i). The size of the second filter may be c×f₂×f₂ in which f₂ refersto the spatial size of the second filter. The number of the secondfilters used in the non-linear mapping may be n₂. Accordingly, then₁-dimensional feature vector outputted from the feature extraction maybe transformed into a n₂-dimensional feature vector after the non-linearmapping. The n₂-dimensional feature vector may include feature value(s)of n₂ features of the first data block, which may be used to reconstructan image.

In the reconstruction stage of the training process, a convolutionoperation may be performed on the n₂-dimensional feature vector of thefirst data block to generate an image corresponding the first datablock. In some embodiments, the image corresponding to the first datablock may be regarded as an actual output of the first data blockgenerated by the convolutional neural network model 1000. Merely by wayof example, the reconstruction of the image may be performed accordingto Equation (8) as below:F(X _(i))=W ₃ *F ₂(X _(i))+B ₃,  (8)wherein W₃ refers to a third filter (e.g., a linear filter); B₃ refersto a third deviation; and F(X_(i)) refers to the reconstructed imagecorresponding to the i^(th) first data block of the input data. The sizeof the third filter may be c×f₃×f₃ in which f₃ refers to the spatialsize of the third filter. In some embodiments, n₂ third filters may beused in the image reconstruction.

In the optimization stage of the training process, an MSE may bedetermined to measure the similarity (or the difference) of thereconstructed data (e.g., one or more reconstructed images correspondingto first data blocks) and the tag data (e.g., one or more second datablocks). The preliminary parameter(s) of the convolutional neuralnetwork model may be adjusted based on the determined MSE. Detailsregarding the cost layer and the loss function may be found elsewhere inthe present disclosure (e.g., FIGS. 9A and 9B, and the relevantdescriptions thereof).

It should be noted that the example illustrated in FIGS. 10 and 11, andthe descriptions thereof are provided for the purposes of illustration,and is not intended to limit the scope of the present disclosure. Forpersons having ordinary skills in the art, multiple variations andmodifications may be made under the teachings of the present disclosure.However, those variations and modifications do not depart from the scopeof the present disclosure. For example, the convolutional neural networkmodel 1000 may include any number of convolutional layers other thanthree. As another example, the convolutional neural network model 1000may adopt another activation function in the activation layer and/oranother loss function in the lost layer. In some embodiments, theconvolutional neural network model 1000 may be a denoising convolutionalneural network (DCNN) model further including one or more normalizationlayers.

FIG. 12 is a schematic diagram illustrating image decompositionaccording to some embodiments of the present disclosure. As described inconnection with FIG. 6, a first image and a second image may bedecomposed into a plurality of sets of decomposition data. The sets ofdecomposition data may further be used for training one or more neuralnetwork model for scattering correction.

For illustration purposes only, the decomposition of an image t0 isdescribed below as an example. In some embodiments, the image t0 may bean exemplary first image that includes scattering noises as described inconnection with FIGS. 5 and 6. As shown in FIG. 12, the image t0 isdecomposed into seven sets of decomposition data, including S0, S1, S2,S3, S4, S5, and S6. The seven sets of decomposition data may correspondto different frequency bands in which the decomposition data S0corresponds to the highest frequency band and the decomposition data S6corresponds to the lowest frequency band. “G↓” represents adown-sampling operation. For example, image t1 is determined byperforming a down-sampling operation on the image to. Similarly, imagest2, t3, t4, t5, and t6 are determined by performing a down-samplingoperation on the images t1, t2, t3, t4, and t5, respectively. “G↑”represents an up-sampling operation. For example, image f5 is determinedby performing an up-sampling operation on the image t6. Similarly,images f4, f3, f2, f1, and f0 are determined by performing anup-sampling operation on the images t5, t4, t3, t2, and t1,respectively.

The sets of decomposition data S0 to S6 may be determined based on theup-sampled and down-sampled images. As shown in FIG. 12, thedecomposition data S0 is a difference image between the images f0 andthe image t0. The decomposition data S1 is a difference image betweenthe images f1 and the image t1. The decomposition data S2 is adifference image between the images f2 and the image t2. Thedecomposition data S3 may is a difference image between the images f3and the image t3. The decomposition data S4 is a difference imagebetween the images f4 and the image t4. The decomposition data S5 is adifference image between the images f5 and the image t5. Thedecomposition data S6 is the image t6. A difference image between twoimages may be determined by subtracting one image from another.

In some embodiments, the sets of decomposition data S0 to S6 may be usedto train a multi-scale convolutional neural network model. Themulti-scale convolutional neural network model may include a pluralityof sub-convolutional neural network models. For example, as illustratedin FIG. 12, a multi-scale convolutional neural network model 1210includes seven sub-convolutional neural networks (illustrated as CNN1,CNN2, CNN3, CNN4, CNN5, CNN6, and CNN7 in FIG. 12). The sets ofdecomposition data S0 to S6 may be respectively inputted into the CNN1to CNN7. During the training process, the preliminary parameter(s) ofeach sub-convolutional neural network model may be adjusted to generatecorresponding model parameter(s). A plurality of trainedsub-convolutional neural network models may be determined based on thesub-convolutional neural network models and their corresponding modelparameters. Details regarding the training of a convolutional neuralnetwork model may be found elsewhere in the present disclosure (e.g.,step 560 of the process 500, FIGS. 7 to 10, and the relevantdescriptions thereof). In some embodiments, the trainedsub-convolutional neural network models may be used to correct images(or a portion thereof) that correspond to different frequency bands,since they are respectively trained according to sets of decompositiondata that correspond to different frequency bands.

FIG. 13 is a flowchart illustrating an exemplary process for correctinga target image according to some embodiments of the present disclosure.The process 1300 may be implemented in the imaging system 100illustrated in FIG. 1. For example, the process 1300 may be stored inthe storage device 150 and/or the storage 220 in the form ofinstructions (e.g., an application), and invoked and/or executed by theprocessing device 140 (e.g., the processor 210 illustrated in FIG. 2, orone or more modules in the processing device 140-C illustrated in FIG.4C). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 1300 maybe accomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 1300 as illustrated inFIG. 13 and described below is not intended to be limiting.

In 1310, the obtaining module 450 may obtain a target image. The targetimage may be an image including scattering noises. For example, thetarget image may be generated according to scan data acquired by theimaging device 110 without the grid. In some embodiments, the obtainingmodule 450 may obtain the target image from one or more components ofthe imaging system 100, such as the imaging device 110, a storage device(e.g., the storage device 150), and/or an external source.

In some embodiments, the target image may further be processed by, suchas the processing device 140-C. The processing of the target image mayinclude an image rotation, an image flipping, an image normalization, animage enhancement, an image filtering, or the like, or any combinationthereof. For example, the target image may be flipped horizontally,vertically, or in any other direction. Alternatively or additionally,the target image may be rotated by an angel. In some embodiments, thetarget image may be normalized with respect to its range of pixel valuesaccording to a normalization technique. Details regarding the imageprocessing (e.g., the image flipping, the image rotation, the imagenormalization) may be found elsewhere in the present disclosure (e.g.,steps 530 and 540 of the process 500, and the relevant descriptionsthereof).

In 1320, the obtaining module 450 may obtain one or more target imagingparameters corresponding to the target image. The target imagingparameters may refer to imaging parameters under which the target imageis generated. Exemplary target imaging parameters may include a tubevoltage, a tube current, a scanning time, an irradiation dose, a slicethickness of scanning, or the like, or any combination thereof. In someembodiments, the obtaining module 450 may obtain the target imagingparameter(s) from one or more components of the imaging system 100(e.g., the imaging device 110, the storage device 150), and/or anexternal source.

In 1330, the obtaining module 450 may obtain one or more trained neuralnetwork models for scattering correction corresponding to one or moresets of imaging parameters. In some embodiments, the trained neuralnetwork models corresponding to various sets of imaging parameters mayrespectively be generated according to the process 500. Each of thetrained neural network models may correspond to a set of imagingparameters. The trained neural network models may be obtained from anexternal source and/or one or more components of the imaging system 100,such as a processing device (e.g., the processing device 140-B), astorage device (e.g., the storage device 150).

In 1340, the selection module 460 may select a target trained neuralnetwork model corresponding to the target imaging parameters among theobtained trained neural network models. For example, the selectionmodule 460 may compare the target imaging parameters with the one ormore sets of imaging parameters, and determine a set of imagingparameters that has the smallest difference with the target imagingparameters. The difference between two sets of imaging parameters may bemeasured by, for example, a total difference, average difference, ormedian value of differences between values of each target imagingparameter and a corresponding image parameter.

In 1350, the correction module 470 may correct the target image usingthe target trained neural network model. In some embodiments, thecorrection module 470 may input the target image into the target trainedneural network model to generate a corrected image. The corrected imagemay include less scattering noises than the target image.

In some embodiments, the target image may be normalized before beinginputted into the target trained neural network model. The correctionmodule 470 may input the normalized target image into the target trainedneural network model to generate a corrected image. The correctionmodule 470 may further perform an inverse normalization on the correctedimage. For example, if the target image is normalized according toEquation (1) as described in connection with step 530 of the process500, the pixel value of each pixel in the corrected image may beinversely normalized by multiplying (I_(max)−I_(min)) and then addingI_(min). As another example, if the target image is normalized accordingto Equation (2) as described in connection with step 530 of the process500, the pixel value of each pixel in the corrected image may beinversely normalized by multiplying nI_(v) and then adding I_(a).

In some embodiments, the target trained neural network model may be atrained multi-scale convolutional neural network model that includes aplurality of trained sub-convolutional neural network models. Thetrained sub-convolutional neural network models may correspond to aplurality of frequency bands as described elsewhere in this disclosure(e.g., FIG. 12 and the relevant descriptions). To correct the targetimage based on the multi-scale convolutional neural network model, thecorrection module 470 may decompose the target image into a plurality ofdecomposed target images. The decomposed target images may correspond tothe same or substantially same frequency bands as the sub-convolutionalneural network models. Each decomposed target image may be inputted intoa trained sub-convolutional neural network model that corresponds to thesame or substantially frequency band to generate a corrected decomposedimage. The correction module 470 may further generate a corrected targetimage based on the plurality of corrected decomposed target images. Forexample, the corrected decomposed target images may be combined into thecorrected target image according to an image blending technique (e.g., aLaplacian blending technique, a Gaussian blending technique). Detailsregarding the combination of corrected decomposed target images may befound elsewhere in this disclosure (e.g., FIG. 14 and the relevantdescriptions).

FIG. 14 shows a schematic diagram for correcting a target image g0 basedon a trained multi-scale convolutional neural network model 1410according to some embodiments of the present disclosure. The trainedmulti-scale convolutional neural network model 1410 includes seventrained sub-convolutional neural network models (i.e., CNN1′, CNN2′,CNN3′, CNN4′, CNN5′, CNN6′, and CNN7′ as shown in FIG. 14). The trainedsub-convolutional neural network models may correspond to variousfrequency bands. In some embodiments, the trained multi-scaleconvolutional neural network model 1410 may be an exemplary trainedneural network model for scattering correction. The trained multi-scaleconvolutional neural network model 1410 may be generated according toexemplary model training techniques described in the present disclosure(e.g., the process 500 and/or FIG. 12).

The target image g0 may be decomposed into a plurality of decomposedtarget images corresponding to the same or substantially same frequencybands as the trained sub-convolutional neural network models. Eachdecomposed target image may further be inputted into a correspondingtrained sub-convolutional neural network mode for correction.

For example, as illustrated in FIG. 14, the target image g0 isdecomposed into seven decomposed target images; that is, L0, L1, L2, L3,L4, L5, and L6. The decomposed target images L0 to L6 respectivelycorrespond to the same or substantially same frequency band as thetrained sub-convolutional neural network models CNN1′ to CNN7′. Thedecomposition of the target image g0 may be performed in a similarmanner as that of the first image t0, and the descriptions thereof arenot repeated here. The decomposed target images L0 to L6 arerespectively inputted into the trained sub-convolutional neural networkmodels CNN1′ to CNN7′ to generate corrected decomposed target images L0′to L6′.

After the correction of the decomposed target images, a corrected targetimage may be generated based on the corrected decomposed target images.For example, the corrected target image may be generated based on animage blending technique. As shown in FIG. 14, image U5 may be generatedby performing an up-sampling operation on the corrected decomposedtarget image L6′. The image U5 may further be added with the correcteddecomposed target image L5′ to generate an image R5. Image U4 may bedetermined by performing an up-sampling operation on the image R5. Theimage U4 may further be added with the corrected decomposed target imageL4′ to generate an image R4. Image U3 may be determined by performing anup-sampling operation on the image R4. The image U3 may further be addedwith the corrected decomposed target image L3′ to generate an image R3.Image U2 may be determined by performing an up-sampling on the image R3.The image U2 may further be added with the corrected decomposed targetimage L2′ to generate an image R2. Image U1 may be determined byperforming an up-sampling on the image R2. The image U1 may further beadded with the corrected decomposed target image LI to generate an imageR1. Image U0 may be determined by performing an up-sampling operation onthe image R1. The image U0 may further be added with the correcteddecomposed target image L0′ to generate an image R0. The image R0 may bethe target image after the correction (also referred to herein as thecorrected target image).

It should be noted that the above example illustrated in FIG. 14 and thedescriptions thereof are merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. However, those variations and modifications do notdepart from the scope of the present disclosure. For example, thetrained multi-scale convolutional neural network model 1410 may includeany number of trained sub-convolutional neural network models, and thetarget image may be decomposed into any number of decomposed targetimages. As another example, the corrected target image may be generatedbased on the corrected decomposed target images according to any othersuitable image blending technique.

FIG. 15A shows an exemplary image 1500A according to some embodiments ofthe present disclosure. FIG. 15B shows an exemplary corrected image1500B according to some embodiments of the present disclosure. Thecorrected image 1500B is generated by correcting the image 1500Aaccording to exemplary image correction techniques disclosed in thepresent disclosure (e.g., the process 1300). As illustrated, thecorrected image 1500B includes less scattering noises than the image1500A. The contrast between the spine 1510 and the lungs 1520 in thecorrected image 1500B are higher than that in the image 1500B.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A non-transitory computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectro-magnetic, optical, or the like, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereofto streamline the disclosure aiding in the understanding of one or moreof the various inventive embodiments. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaimed object matter requires more features than are expressly recitedin each claim. Rather, inventive embodiments lie in less than allfeatures of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate,” or “substantially” may indicate ±20% variationof the value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A system, comprising: a storage device storing aset of instructions; and at least one processor configured tocommunicate with the storage device, wherein when executing the set ofinstructions, the at least one processor is configured to direct thesystem to: generate a trained neural network model for scatteringcorrection corresponding to one or more imaging parameters, the trainedneural network model being trained using training data, wherein togenerate the training data, the at least one processor is configured todirect the system to: obtain a first image corresponding to the one ormore imaging parameters; obtain a second image, the second imageincluding less scattering noises than the first image; determine aplurality of sets of first decomposition data by decomposing the firstimage; determine a plurality of sets of second decomposition data bydecomposing the second image; and determine the training data based onthe plurality of sets of first decomposition data and the plurality ofsets of second decomposition data.
 2. The system of claim 1, wherein:the second image is generated according to second scan data acquired byan imaging device configured with a grid under the one or more imagingparameters, and the first image is generated according to first scandata acquired by the imaging device without the grid under the one ormore imaging parameters, or the first image is generated according tothe first scan data acquired by the imaging device without the gridunder the one or more imaging parameters, and the second image isgenerated by removing at least some of scattering noises of the firstimage, or the first image and the second image are simulated accordingto a Monte-Carlo technique under the one or more imaging parameters. 3.The system of claim 1, wherein a count of the plurality of sets of firstdecomposition data is equal to a count of the plurality of sets ofsecond decomposition data.
 4. The system of claim 1, wherein: thedecomposing of the first image is performed according to a frequency ofat least one of the first image or image data related to the firstimage, and the decomposing of the second image is performed according toa frequency of at least one of the second image or image data related tothe second image.
 5. The system of claim 1, wherein: the decomposing ofthe first image is performed according to at least one of a waveletdecomposition technique or a Laplacian decomposition technique, and thedecomposing of the second image is performed according to at least oneof the wavelet decomposition technique or the Laplacian decompositiontechnique.
 6. The system of claim 1, wherein to determine the trainingdata based on the plurality of sets of first decomposition data and theplurality of sets of second decomposition data, the at least oneprocessor is configured to direct the system to: generate, based on theplurality of sets of first decomposition data and the plurality of setsof second decomposition data, a plurality of matching pairs ofdecomposition data, each of the plurality of matching pairs ofdecomposition data including a set of first decomposition data and acorresponding set of second decomposition data; and determine thetraining data based on at least one of the plurality of matching pair ofdecomposition data.
 7. The system of claim 1, wherein: the plurality ofsets of first decomposition data include a plurality of first decomposedimages corresponding to a plurality of frequency bands respectively; andthe plurality of sets of second decomposition data include a pluralityof second decomposed images corresponding to the plurality of frequencybands respectively.
 8. The system of claim 7, wherein to generate thetrained neural network model for scattering correction, the at least oneprocessor is configured to direct the system further to: determine, foreach of the plurality of frequency bands, a relationship between a firstdecomposed image corresponding to the frequency band and a seconddecomposed image corresponding to the frequency band; and determine thetrained neural network model for scattering correction based ondetermined relationships for the plurality of frequency bands.
 9. Thesystem of claim 1, wherein the training data include input data and tagdata, and the at least one processor is configured to direct the systemfurther to: select a first sub-area from the first image; designateimage data related to the first sub-area as the input data; select, fromthe second image, a second sub-area corresponding to the first sub-area;and designate image data related to the second sub-area as the tag data.10. The system of claim 1, wherein to generate the trained neuralnetwork model for scattering correction, the at least one processor isconfigured to direct the system further to: divide the input data into aplurality of first data blocks; divide the tag data into a plurality ofsecond data blocks corresponding to the plurality of first data blocksrespectively; determine first gradient information of the plurality offirst data blocks; determine second gradient information of theplurality of second data blocks; determine a relationship between theinput data and the tag data based on the first gradient information andthe second gradient information; and generate the trained neural networkmodel for scattering correction based on the relationship between theinput data and the tag data.
 11. A system, comprising: a storage devicestoring a set of instructions; and at least one processor configured tocommunicate with the storage device, wherein when executing the set ofinstructions, the at least one processor is configured to direct thesystem to: obtain a target image; obtain one or more target imagingparameters corresponding to the target image; select, among a pluralityof candidate trained neural network models for scattering correction, atarget trained neural network model for scattering correctioncorresponding to the one or more target imaging parameters, theplurality of candidate trained neural network models corresponding todifferent sets of candidate imaging parameters respectively; decomposethe target image into a plurality of decomposed target imagescorresponding to a plurality of frequency bands respectively; determinea plurality of corrected decomposed target images by correcting theplurality of decomposed target images using the target trained neuralnetwork model for scattering correction; and generate a corrected targetimage based on the plurality of corrected decomposed target images. 12.The system of claim 11, wherein one of the plurality of candidatetrained neural network models for scattering correction is trained usingtraining data, wherein to generate the training data, the at least oneprocessor is configured to direct the system to: obtain a first imagecorresponding to the one or more imaging parameters; obtain a secondimage, the second image including less scattering noises than the firstimage; determine a plurality of sets of first decomposition data bydecomposing the first image; determine a plurality of sets of seconddecomposition data by decomposing the second image; and determine thetraining data based on the plurality of sets of first decomposition dataand the plurality of sets of second decomposition data.
 13. The systemof claim 12, wherein the plurality of sets of first decomposition datainclude a plurality of first decomposed images corresponding to aplurality of frequency bands respectively; the plurality of sets ofsecond decomposition data include a plurality of second decomposedimages corresponding to the plurality of frequency bands respectively;and to generate the trained neural network model for scatteringcorrection, the at least one processor is configured to direct thesystem further to: determine, for each of the plurality of frequencybands, a relationship between a first decomposed image corresponding tothe frequency band and a second decomposed image corresponding to thefrequency band; and determine the trained neural network model forscattering correction based on determined relationships for theplurality of frequency bands.
 14. The system of claim 12, wherein thetraining data include input data and tag data, and the at least oneprocessor is configured to direct the system further to: select a firstsub-area from the first image; designate image data related to the firstsub-area as the input data; select, from the second image, a secondsub-area corresponding to the first sub-area; and designate image datarelated to the second sub-area as the tag data.
 15. A method implementedon a computing device including a storage device and at least oneprocessor, comprising: generating a trained neural network model forscattering correction corresponding to one or more imaging parameters,the trained neural network model being trained using training data,wherein to generate the training data, the method further includes:obtaining a first image corresponding to the one or more imagingparameters; obtaining a second image, the second image including lessscattering noises than the first image; determining a plurality of setsof first decomposition data by decomposing the first image; determininga plurality of sets of second decomposition data by decomposing thesecond image; and determining the training data based on the pluralityof sets of first decomposition data and the plurality of sets of seconddecomposition data.
 16. The method of claim 15, wherein: the secondimage is generated according to second scan data acquired by an imagingdevice configured with a grid under the one or more imaging parameters,and the first image is generated according to first scan data acquiredby the imaging device without the grid under the one or more imagingparameters, or the first image is generated according to the first scandata acquired by the imaging device without the grid under the one ormore imaging parameters, and the second image is generated by removingat least some of scattering noises of the first image, or the firstimage and the second image are simulated according to a Monte-Carlotechnique under the one or more imaging parameters.
 17. The method ofclaim 15, wherein a count of the plurality of sets of firstdecomposition data is equal to a count of the plurality of sets ofsecond decomposition data.
 18. The method of claim 15, wherein: thedecomposing of the first image is performed according to a frequency ofat least one of the first image or image data related to the firstimage, and the decomposing of the second image is performed according toa frequency of at least one of the second image or image data related tothe second image.
 19. The method of claim 15, wherein: the decomposingof the first image is performed according to at least one of a waveletdecomposition technique or a Laplacian decomposition technique, and thedecomposing of the second image is performed according to at least oneof the wavelet decomposition technique or the Laplacian decompositiontechnique.
 20. The method of claim 15, wherein the determining thetraining data based on the plurality of sets of first decomposition dataand the plurality of sets of second decomposition data, the methodfurther includes: generating, based on the plurality of sets of firstdecomposition data and the plurality of sets of second decompositiondata, a plurality of matching pairs of decomposition data, each of theplurality of matching pairs of decomposition data including a set offirst decomposition data and a corresponding set of second decompositiondata; and determining the training data based on at least one of theplurality of matching pair of decomposition data.